A dynamic migration model for self-adaptive genetic algorithms

Authors:
K. G. Srinivasa;K. Sridharan;P. Deepa Shenoy;K. R. Venugopal;Lalit M. Patnaik
Affiliations:
Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore;Department of CSE, Sunny Buffalo;Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore;Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore;Microprocessor Applications Laboratory, Indian Institute of Science, India
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

Citing 2
Cited 3

Self-Adaptive Genetic Algorithm for Numeric Functions

PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Intelligent Data Analysis
Selective dissemination of XML documents using GAs and SVM

CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Indexing XML documents using self adaptive genetic algorithms for better retreival

APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).